
Prospective Directions to Improve Model Generalizability.
There are two major challenges of generalizability, which illuminates two potential directions to improve model performance: behavior change of an individual across time, and behavior differences between individuals.
Compared to other cross-dataset setups, the setup of overlapping users has a relative performance advantage (see Table REF ). This indicates that addressing temporal shifts along a single individual's longitudinal behavior could be a relatively easier task. Some recent algorithms such as AdaRNN [1]} are designed to address this challenge and are worth testing.
As for the individual difference, Reorder indicates that leveraging a pre-text shuffling and reordering task may push the model to learn more generalizable representations. This suggests that designing more pre-text tasks that can capture the nature of human behavior could be another future direction, e.g., a task to predict the immediate next behavior feature value (analogous to the pre-text task of BERT [2]}).

